Arctic sea ice has
not only decreased in volume during the last decades, but has also changed in
its physical properties towards a thinner and more seasonal ice cover. These
changes strongly impact the energy budget, and might affect the
ice-associated ecosystems. In this study, we quantify solar shortwave fluxes
through sea ice for the entire Arctic during all seasons. To focus on
sea-ice-related processes, we exclude fluxes through open water, scaling
linearly with sea ice concentration. We present a new parameterization of
light transmittance through sea ice for all seasons as a function of variable
sea ice properties. The maximum monthly mean solar heat flux under the ice of
30
The evolution of Arctic sea ice towards a thinner, younger, and more seasonal sea ice cover during the last few decades (e.g., Comiso, 2012; Haas et al., 2008; Maslanik et al., 2007, 2011) has a strong impact on the partitioning of solar energy between the atmosphere, sea ice, and ocean (e.g., Perovich et al., 2007b, 2011a; Wang et al., 2014). Decreased surface albedo (Perovich et al., 2011a), earlier melt onset, and a longer melt season (Markus et al., 2009, updated) have contributed to the observed increases in sea ice and snow melt (Perovich and Richter-Menge, 2009), and higher absorption and transmission of solar irradiance within and through Arctic sea ice (Nicolaus et al., 2012; Stroeve et al., 2014). Beyond the physical consequences of the observed changes, strong impacts on ecological interactions and biogeochemical processes are expected, such as changes in habitat conditions for ice-associated organisms or changes in primary production (Arrigo et al., 2012; Deal et al., 2011; Popova et al., 2012).
Various studies have shown the immediate link between sea ice energy and mass balance, as well as the impact of energy fluxes on the physical properties of sea ice (Grenfell et al., 2006; Light et al., 2008; Perovich and Richter-Menge, 2009). These heat fluxes are composed of short-wave, long-wave, conductive, and turbulent fluxes at the interfaces of sea ice with the atmosphere and the ocean. Beyond these energy budget approaches, sea ice mass balance may also be derived from direct comparisons of sea ice growth during winter, and surface and bottom melt during summer (Perovich et al., 2011b).
From studies on the interaction of sunlight and sea ice, it has been possible to improve our understanding of the effects of snow cover (Perovich et al., 2007b), melt ponds (Rösel and Kaleschke, 2012; Schröder et al., 2014), and biological interactions (Arrigo et al., 2012; Mundy et al., 2005, 2007). In addition, the spatial variability (Perovich et al., 2011a) and seasonal changes (Nicolaus et al., 2010a; Perovich et al., 2002; Perovich and Polashenski, 2012) in the optical properties of sea ice and snow have been studied by different methods. However, previous studies have not quantified large-scale, multi-seasonal, and inter-annual changes, because these studies were limited to different regions and/or seasons of the year. In addition, these studies have described measurements on different ice types, which also differ in their optical properties as a result of their growth history (Perovich and Polashenski, 2012). One possible approach obtaining such generalized studies on the in- and under-ice energy budgets in sea-ice-covered oceans would be to use a radiative transfer model in combination with surface energy budgets, as implemented by Perovich et al. (2011a). However, such a model would require adequate knowledge about the distribution of snow and sea ice (as forcing data) to derive the optical properties of sea ice and snow as a function of space and time. This type of information is not available yet, in particular not for timescales on the order of decades. An alternative approach is to use existing remote sensing and re-analysis data together with a parameterization of light transmittance through sea ice. This method was developed by Nicolaus et al. (2012, 2013) to calculate Arctic-wide radiation fluxes through sea ice. However, these studies were restricted to 1 month (August 2011) when comprehensive in situ measurements are available from the trans-polar cruise of German research vessel Polarstern.
In order to improve the understanding of the ongoing change in sea ice
conditions and the associated impact on the partitioning of solar energy, we
provide an estimate of the monthly shortwave radiative transfer through sea
ice for the entire Arctic Ocean for the period 1979 to 2011. To emphasize the
changing physical properties of the Artic sea ice cover, our estimates
include fluxes through sea ice only. Therefore, we use a definition of 6
types of sea ice over the annual cycle, define 6 distinct time periods of
insolation conditions, and include the temporal and spatial variability of
melt ponds to extend and generalize the upscaling method of Nicolaus et
al. (2012, 2013). In order to investigate the reliability of the method and
to obtain a measure of uncertainty, we perform sensitivity studies by
comparing the calculated fluxes to in situ observations obtained from the
Transpolar Drift, between 86.5 and 88.5
Solar short-wave radiation fluxes (250 to 2500 nm, here also referred to as
“light”) through sea ice are calculated daily, from 1 January 1979 to
31 December 2011, for the entire Arctic Ocean (north of 65
For the main analyses, we exclude open water areas, as those would clearly dominate the transmitted heat flux signal (Perovich et al., 2007a). Therefore, we consider only fluxes through ice-covered areas, as these are crucial for the energy and mass balance of sea ice as well as for biological processes beneath the ice cover. The solar heat input to the open ocean also has an important impact on the ice–ocean system, but is a basic function of sea ice concentration.
Solar heat input through sea ice into the ocean (
Classification of sea ice
Since 1 January 2000, when satellite-derived melt-pond concentrations are
available, the solar heat input through sea ice into the ocean
(
To obtain the total solar heat input per unit area for a certain time period
(
Assuming sea ice is at its melting point, has a density
To calculate solar heat fluxes under Arctic sea ice for an entire year, the
main challenge is to parameterize the seasonal evolution of
Figure 1 shows the annual cycle of these six sea ice classes together with
surface properties of Arctic sea ice. These classes are introduced to avoid
abrupt changes in the optical properties during the transition from spring to
summer as well as from summer to fall. After early melt onset (EMO),
In the following, sea ice consisting of both bare sea ice and melt ponds is called pond-covered sea ice.
The seasonal evolution of surface properties and the transmittance of Arctic
sea ice is divided into six different phases (note that there are both
different ice types and different seasonal phases). The timing of these
phases is based on the melt and freeze onset data established by Markus et
al. (2009, updated). Our parameterization of seasonal variations in light
transmittance considers the transmission through both sea ice and snow, and
is mostly based on the results of two field campaigns that focused on the
understanding of ice–ocean–atmosphere processes that control the
partitioning of solar radiation between reflection, absorption, and
transmittance: the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment
from 1997 to 1998 (Perovich, 2005), and measurements conducted on MYI within
the Transpolar Drift, between 86.5 and 88.5
Winter conditions are characterized by snow-covered sea ice without melt ponds. The snow cover is assumed to be cold, dry and optically thick, which means the snow determines the optical properties. Thus, radiative fluxes through sea ice are small. The best available transmittance observations for such conditions are those measured during the first days of the Tara drift, although it was already early April. Hence, transmittance was accordingly set to 0.002 (Nicolaus et al., 2010a).
EMO denotes the first significant change in optical properties. Snow depth
decreases, and surface and sea ice temperatures increase. Consequently, the
snow becomes wet and is no longer optically thick. This phase also
corresponds to formation of the first melt ponds. Here we assume a linear
increase in
Nicolaus et al. (2010a) calculated a transmittance of 0.02 for MYI for the day of MO. Furthermore, Perovich and Polashenski (2012) reveal that the surface albedo of FYI is about half that of MYI at the same time. Adapting this albedo evolution to the transmittance, the transmittance of FYI is assumed to be 0.04 at MO.
After EMO, the continued melt of snow and sea ice strongly impacts light
transmittance. Starting with the summer phase (Phase IV), we assume that the
optical properties of melting sea ice differ from sea ice surviving the
summer melt. In addition, differences between melting FYI and melting MYI are
expected. Therefore, melting FYI and melting MYI are separated in the
parameterization of
In order to describe these classes, laboratory studies by Perovich (1996) on the evolution of albedo during the initial ice growth phase were applied to the evolution of transmittance, assuming an inverse behavior of transmittance and albedo. Therefore, the increase in transmittance of seasonal sea ice can be described as roughly exponential (Perovich, 1996). Assuming the transition of transmittance from melting sea ice to the open ocean is the inverse of the albedo transition (Perovich, 1996), we use a transmittance of 0.4 for the last remaining sea ice. Thus, an exponential increase between the first and last day of melting for the corresponding pixel is fitted, and the maximum transmittance of sea ice is expected to be 0.4.
After MO, snow is assumed to melt completely within 14 days (Nicolaus et al.,
2006; Perovich et al., 2002), and pond cover fraction increases rapidly until
the maximum pond cover is reached at the end of this phase (Nicolaus et al.,
2010a). The transmittance continues to increase linearly until the beginning
of summer (MO
During this phase, the sea ice surface is characterized by strong sea ice
melt and culminates in the minimum ice concentration of each pixel. The
surface is a mixture of bare ice and melt ponds with a constantly renewing
surface scattering layer (Perovich et al., 2002; Barber et al., 1998). This
implies small changes in the optical properties and light transmittance of
the ice over time during Phase IV. Hence,
Air and surface temperatures drop below 0
This phase is characterized by continuous freezing, increasing snow
accumulation towards an optically thick snow layer, and the gradual
disappearance of melt ponds. In addition to new sea ice formation, the
existing sea ice is getting thicker and older, and deformation is increasing.
Transmittance decreases back to 0.02 by winter. It is assumed that at the end
of the freezing phase (FO
For the period after the year 2000, when satellite-derived melt pond products
are available from Rösel and Kaleschke (2012), the transmittance values
of bare ice (
Based on the calculated results of the solar heat input through sea ice into
the ocean, trends are analyzed for the period 1979 to 2011. The trends
(monthly and annual) are calculated by a linear least-squares fit of the
total mean (monthly or annual) heat flux for each grid cell
Transmittance values of different sea ice and surface types. Abbreviations: FYI: first year ice; MYI: multi-year ice; Phase I: winter; MO: melt onset; Phase IV: summer; FO: freeze onset; Threshold: transition from open ocean to sea ice and vice versa.
Data sources of the different parameters used in this study.
Monthly mean of total solar heat input (
The following satellite and re-analyses data sets were used (Table 2):
Sea ice concentration observations were obtained from the Special Sensor
Microwave Imager (SSMI/S) provided through the Ocean and Sea Ice Satellite
Application Facilities (OSI SAF, product ID OSI-401, Andersen et al., 2007).
For this study, a combination of reprocessed data (1979 to 2007) and
operational data (2008 to 2011) was used. Both data sets have systematic
differences due to processing with a different set of tie point statistics
for the ice concentration algorithm (Lavergne et al., 2010). However, within
the documented uncertainties, both data sets build the best available and
consistent time series of sea ice concentration. There is no consistent
uncertainty for the data product, but different approaches for determining
uncertainties are described in Lavergne et al. (2010). For sea ice age, we used the updated data product by
Maslanik et al. (2007, 2011). This product has been available since 1979, and
is based on satellite-derived ice motion data calculated from different
sensors using a Lagrangian feature tracking algorithm. Although this data
product distinguishes ice ages between 1 and 10 years, here we only
distinguish FYI and MYI (2 years and older), because all MYI is assumed to
have similar optical properties. All data points with a sea ice concentration
of greater than 0 but without an assigned sea ice age class were treated as
FYI. Vice versa, all data points with sea ice concentration of greater than 15 %
but which had an assigned sea ice age class were treated as open
water. Such modifications were necessary to obtain consistent data products
from the different sources, indicating partially varying sea ice extents. The
ice age data set represents a 7-day average of either FYI or MYI without any
uncertainty estimates. However, uncertainties in sea ice concentration and
drift will have an impact on the ice age data. Downward surface solar radiation data were obtained four times per day
from the European Centre for Medium-Range Weather Forecast (ECMWF)
Era-Interim re-analyses (Dee et al., 2011; Lindsay et al., 2014). The data
(four values per day) were averaged to daily means and have been available
since 1979. Uncertainties for the data set are not reported. Sea ice surface characteristics were categorized by melt and freeze
onset dates from passive microwave data (1979 to 2012) (Markus et al., 2009,
updated). The data set distinguishes between the first occurrence of a melt
event (early melt onset, EMO), the following continuous melt (melt onset,
MO), the first occurrence of freeze-up conditions (early freeze onset, EFO),
and the day of persistent freezing conditions (freeze onset, FO). The
standard deviations, assumed as uncertainties, for the given dates are
reported as EMO Melt pond fraction was used from Rösel et al. (2012),
retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS)
onboard NASA's Terra and Aqua satellites. As this data set has only been
available since 2000, melt pond fractions from 1979 to 1999 were set to
constant summer mean values of 26 % for FYI and 29 % for MYI, as given in
Rösel et al. (2012) for August 2011. In order to maintain the consistency
of the surface characteristics, all melt pond fractions before EMO are set to
zero. The mean standard deviation from 2000 to 2011, assumed as uncertainty,
is calculated as
We do not include snow depth and sea ice thickness as input data sets due to the lack of consistent high temporal resolution and long-term data products. Limitations of using sea ice age as an indirect proxy for ice thickness and snow cover as well as potential other approaches for the estimation of transmitted heat fluxes are discussed below.
Annual total solar heat input (
Based on the availability of all input data sets and the seasonality of
transmittance values, the solar heat input through sea ice into the ocean is
analyzed from 1979 to 2011. Figure 3 shows monthly mean heat input
(
Arctic-wide total solar heat flux under sea ice (
The new data set of
The mean trend of
The total annual solar radiation under Arctic sea ice was estimated to be
53.3
Comparing our results to the development of the solar heat input into the ice
presented by Perovich et al. (2011a, Fig. 2), both the solar heat input to
the upper ocean and the solar heat input to the sea ice demonstrated a
positive annual trend of 1 to 1.5 % yr
The trend towards more light transmission through sea ice does not only impact the light conditions right at the bottom of the sea ice, but also affects the horizontal and vertical light field in the ice-covered ocean. More light at the bottom of sea ice will deepen the euphotic zone, as more light penetrates deeper into the ocean (Frey et al., 2011; Katlein et al., 2014). More light can contribute to an increase in mixed layer temperature, and provide more energy for primary production and biogeochemical processes in and beneath the sea ice. However, it has to be noted that an increase in light availability does not necessarily increase biological activity, and might also be harmful (Leu et al., 2010).
An increase in transmittance will accelerate internal and bottom melt, which in turn will reduce the thickness of sea ice and increase transmittance. That feedback process can trigger a transmittance-melt feedback.
All presented trends are normalized with the trend in sea ice concentration
(Sect. 2.3). Thus, changes related to physical properties of the sea ice are
highlighted instead of changes related to a general sea ice retreat. Fluxes
through the ice-covered ocean will be of great importance, and are much more
difficult to assess than fluxes through open water. However, including the
trend in sea ice concentration, the annual trend of transmitted solar heat
fluxes to the upper ocean decreases from
Beyond this, it is also important to consider that the trends in sea ice
concentration differ significantly during different months. While it is
largest (
Validation of the calculated trends and spatial variability is nearly impossible, as insufficient field data with adequate spatial and temporal coverage are available. However, some comparisons with time series of light transmission from different field studies may be performed to identify major uncertainties.
Here, we compare the surface and transmitted solar irradiance of the
presented method with in situ measurements during the Transpolar Drift of
Tara from 29 April to 28 August 2007 (Nicolaus et al., 2010a).
Nearest-neighbor grid points within 0.5
After 14 August, the measured transmitted heat flux increased rapidly to
about 6 Wm
The main reason for these differences is the timing of the phases describing the surface characteristics. While both data sets have a coincident EMO on 9 June, large differences are evident for the later phase transitions: the observed MO at Tara was on 21 June, whereas the calculated MO for the center position was 17 days later on 8 July. Considering the eight neighboring cells results in a mean MO on 13 June. This shows that there is a difference of 25 days in MO for the 10 km grid. As presented above, the transmitted heat flux depends strongly on the timing of the different melt phases by Markus et al. (2009). EFO was observed on 15 August during Tara, whereas the satellite data maintain summer melt conditions until 14 September. However, the total solar heat input through sea ice was similar for both data sets. Thus, the solar radiation flux under Arctic sea ice depends strongly on the timing of EMO and MO, while the timing of EFO and FO seems to be of less importance, since the beginning of the melt season coincides with maximum surface solar heat fluxes. The timing of melt onset also has a large influence on the total amount of light absorption, as shown in Stroeve et al. (2014). Including the ongoing lengthening of the melt season by up to 2 weeks per decade (by a later EMO), Stroeve et al.'s (2014) calculations suggest an albedo decrease of 9 % per decade.
In a second validation step, the heat fluxes were re-calculated using the
onset dates as observed during Tara instead of those by Markus et al. (2009)
(Fig. 6, black lines). This eliminated the impact of the onset dates on the
results. Nevertheless, the calculated total solar heat input through sea ice
still differed by 18 % (25.4 MJ m
Hudson et al. (2013) measured heat fluxes and calculated transmittance values of Arctic FYI in July/August 2012. However, a direct comparison of energy fluxes, as for the Tara measurements, is not possible, because the melt-pond concentration data set ends in December 2011. August transmittance in our study (0.087) is based on the observations by Nicolaus et al. (2012), which is only half of the 0.16 found by Hudson et al. (2013). Hence, it may be assumed that heat fluxes through sea ice would be larger, based on those measurements. Differences between both studies mainly result from differences in sea ice thickness during the respective campaigns as well as the different methods of quantifying transmittance (mean value vs. modal value) (Hudson et al., 2013).
Measurements from ice-tethered profilers (ITPs) (Krishfield et al., 2008) could be used as an alternative approach to estimate uncertainties of the new parameterization. They allow quantifcation of the heat content of the uppermost ocean and its changes. However, such a comparison would require a significant extension of the present study, integrating radiation fluxes to larger depths and through open water. Similarly, the inclusion of a radiation transfer model is beyond the aim of this study. The advantage of this study is the rather simplistic approach based on a seasonal parameterization of under-ice fluxes applied to existing large-scale data products.
An improvement to this study would be the inclusion of sea ice thickness (e.g., CryoSat-2, IceSat, OperationIceBridge) and snow depth (e.g., AMSR-E) observations from satellites. As with all other input data, the above-mentioned products need to be consistent over many years and reliable during all seasons. However, this is not the case yet, and even the most recent data sets have huge uncertainties or are not available after melt onset (e.g., Ricker et al., 2014), which is the most important time with respect to transmitted heat fluxes. Hence, these parameters are not applicable for such parameterizations yet. Instead, sea ice age is used as a proxy for ice thickness and snow depth distribution. It also includes information about roughness and deformation of the sea ice surface. These characteristics are crucial for the description of optical properties of sea ice.
In addition, including data sets of different model simulations, such as sea ice thickness, snow depth, and melt pond fraction (e.g., Flocco et al., 2012; Schröder et al., 2014), can be considered to be an alternative approach for the presented calculations.
Another uncertainty in the presented heat flux calculations results from constant values for the transmittance of melt ponds on FYI and MYI. Based on our existing data, it was not possible to include a seasonality in melt pond transmittances, which represents the different formation and evolution stages (Perovich and Polashenski, 2012). However, the applied transmittances of melt ponds are modal values of a distribution function (Nicolaus et al., 2012), representing a range of possible values. This has to be considered when comparing our fluxes to other observations or model results. Overall, we expect that the uncertainties resulting from the missing seasonal cycle will have a much smaller impact than the timing of melt onset, which is discussed in the next section.
Based on uncertainties of the independent input variables (timing and length of the melt season, ice age, and melt pond fraction), several sensitivity studies have been performed to estimate the uncertainty for the presented parameterization.
The first study studies the effect of altering the timing and duration of the melt season on the solar heat input to the upper ocean. Three cases are discussed: shifting the melt season dates by (Case 1a) the average uncertainty of 4 days, as given by Markus et al. (2009), (Case 1b) 7 days based on the temporal resolution of ice age data (once per week) (Maslanik et al., 2011), and (Case 1c) (averaged) 14 days, as derived from comparisons with the Tara field data (Nicolaus et al., 2010a). Based on the observed ongoing trend towards a lengthening of the melt season, all sensitivity studies were only performed for earlier EMO and MO, and a later EFO and FO for the exemplary year of 2011.
Extending the melt season by 4 days (Case 1a) results in Arctic-wide mean EMO
on 12 May and MO on 27 May. This affects most regions primarily during
periods of high sea ice concentration and large surface solar irradiance. It
results in an increase in total annual solar heat input through sea ice to
the ocean (
Including 7 days earlier EMO and MO (8 May and 23 May, respectively)
(Case 1b) result in an additional heat input of 5.9
Annual Arctic-wide solar heat input (and relative changes) under sea
ice (
Changes in annual total solar heat input (
Extending the melt season by 14 days later EFO and FO (Case 1c) (21 October
and 2 November, respectively) result in a 1 % increase in
In a second sensitivity study, the influence of the ice type was quantified.
As the sea ice type data contain no uncertainty, the study is based on the
ongoing trend towards a predominantly FYI-covered Arctic Ocean. The reference
ice cover of 2011 consists of 56 % FYI and 44 % MYI in August 2011.
Assuming that all sea ice in 2011 was MYI, the mean transmitted flux
decreased by 34 % to 35.5
The third sensitivity study investigates the effects of melt pond fraction uncertainties. Here, we consider two cases: (Case 3a) Rösel et al. (2012) give a mean uncertainty of 3 %, and (Case 3b) we estimate an uncertainty of 20 % due to the neglected seasonal cycle. Adapting these assumptions, an increasing melt pond fraction of 3 % (20 %) results in an increase in the transmitted heat flux of 1 % (9 %).
Uncertainties in the solar surface radiation and sea ice concentration are not analyzed through additional sensitivity studies, because they impact the results linearly (Eq. 2).
The presented parameterization for light transmission through Arctic sea ice
in combination with satellite-derived time series observations and
re-analysis data allowed the quantification of solar short-wave radiation
fluxes through Arctic sea ice for the entire annual cycle over 33 years (1979
to 2011). The presented results suggest that 96 % of the total annual solar
heat input through sea ice occurs over only 4 months (May to August), with
the highest transmitted fluxes calculated for June. Over the time period 1979
to 2011, an increase in light transmission of 1.5 % yr
This study considers the fluxes through ice-covered ocean regions only. This highlights the fact that changes in sea ice properties have a large impact on the sea ice and upper ocean energy budget, and that this impact adds to the obvious increase in energy input resulting from the observed decrease in ice-covered areas (open ocean effect). However, the ongoing retreat of sea ice will cause additional increases in radiation fluxes into the Arctic Ocean. The additional heat will also contribute to an increase in heat stored in the ocean mixed layer, and will impact the melt season duration and timing, particularly during autumn freeze-up.
A comparison of trends in solar heat fluxes into the sea ice by Perovich et al. (2011a) with our calculated solar heat fluxes through sea ice suggests similar increases in transmitted and absorbed energy. This additional energy input into the sea ice and the upper ocean would also impact inner sea ice structures as well as internal and basal melting. Studies from Perovich et al. (2011a) and Nicolaus et al. (2012, 2013) reveal that fluxes through open water clearly dominate the transmitted heat flux signal and, therefore, the effect of sea ice concentration becomes most obvious. Since our presented study focuses on changes in physical properties of sea ice and its effects, all calculated trends are corrected for the trend in sea ice concentration, and fluxes through open water are neglected. Also, the effects of heat convection and advection as well as lateral heat fluxes are not discussed, due to the limited number of recent studies on that topic.
More investigations of bio-geo-physical interactions are needed to quantify better the effects of the changing physical environment on the ecosystem and element cycles, and vice versa. Additional work is also required to improve Arctic-wide snow depth and sea ice thickness data products. Those products should provide a good description of surface properties during the spring–summer transition, when the largest uncertainties were found. Such time series might become available from new data products that merge observations from different satellites and sensor types (e.g., SMOS, CryoSat-2, AMSR-E), and potentially also numerical models. The non-existence of such reliable long-term and Arctic-wide data sets was the main motivation for developing the presented method, based on available parameters. Otherwise, the application of a radiation transfer model with adequate input (forcing) data would have been an obvious alternative.
We are most grateful to Jim Maslanik (University of Colorado Boulder), Thorsten Markus and Jeffrey Miller (both NASA Goddard Space Flight Center), Thomas Lavergne (OSISAF, Met Norway), and Anja Rösel and Larsch Kaleschke (both University of Hamburg) for data provision and support through manifold discussions on their data products and processing details. We thank Christian Katlein (Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung) and Martin Claussen (Max Planck Institute for Meteorology) for constructive comments on the manuscript, as well as Benjamin Lange (Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung) for proofreading. We appreciate the efforts of the three anonymous reviewers and the scientific editor in improving the manuscript. The study was funded through the Remote Sensing Alliance of the Helmholtz Association and the Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung. Edited by: R. Lindsay